Monitoring effects of heavy metal stress on biochemical and spectral parameters of cotton using hyperspectral reflectance

Deep learning based abiotic crop stress assessment for precision agriculture: A comprehensive review

Abiotic stresses are a leading cause of crop loss and a severe peril to global food security. Precise and prompt identification of abiotic stresses in crops is crucial for effective mitigation strategies. In recent years, Deep learning (DL) techniques have demonstrated remarkable promise for high-throughput crop stress phenotyping using remote sensing and field data. This study offers a comprehensive review of the applications of DL models like artificial neural networks (ANN), convolutional neural networks (CNN), recurrent neural networks (RNN), vision transformers (ViT), and other advanced deep learning architectures for abiotic crop stress assessment using different modalities like IoT sensor data, thermal, spectral, RGB with field, UAV and satellite based imagery. The study comprehensively analyses the abiotic stress conditions due to (a) water (b) nutrients (c) salinity (d) temperature and (e) heavy metal. Key contributions in the literature on stress classification, localization, and quantification using deep learning approaches are discussed in detail. The study also covers the principles of deep learning models, and their unique capabilities for handling complex, high-dimensional datasets inherent in abiotic crop stress assessment. The review also highlights important challenges and future directions in deep learning based abiotic crop stress assessment like limited labelled data, model interpretability, and interoperability for robust stress phenotyping. This study critically examines the research pertaining to the abiotic crop stress assessment, and provides a comprehensive view of the role deep learning plays in advancing abiotic crop stress assessment for data-driven precision agriculture.

Mining soil heavy metal inversion based on Levy Flight Cauchy Gaussian perturbation sparrow search algorithm support vector regression (LSSA-SVR)

Soil heavy metal pollution in mining areas poses severe challenges to the ecological environment. In recent years, machine learning has been widely used in heavy metal inversion by hyperspectral data. However, deterministic algorithms and probabilistic algorithms may confront local optimal solutions in practical applications. The local optimal solution is not the optimal value obtained within the entire defined interval, and as a result will affect the reliability of these approaches. This paper proposes a Levy Flight Cauchy Gaussian perturbation Sparrow Search algorithm Support Vector Regression (LSSA-SVR) soil heavy metal content prediction model. It introduces Levy Flight (LF) measurement and Cauchy Gaussian perturbation based on the Sparrow search algorithm. The LSSA-SVR model was shown to increase the breadth of solutions searched, avoiding the local optimal solution problem. When applied to mining soil heavy metal experiments, we found that the LSSA-SVR model gave a good fit for the elements Cu, Zn, As, and Pb. The correlation coefficients between the predicted results and the actual results of the four elements were all above 0.94. The heavy metal predicted results of LSSA-SVR have a small error margin in both the overall distribution and in individual differences. This study provides an efficient and accurate monitoring method for mining soil heavy metal inversion. It also provides strong support for environmental management and soil remediation.

Toxic effects of atmospheric deposition in mining areas on wheat seedlings

Storage and transportation of coal, as well as operation of coal-fired power plants, produce amounts of metallic exhaust that may lead to different atmospheric environment in the overlapped areas of farmland and coal resource (OAFCR) environment. To investigate the effects of different atmospheric environment in the OAFCR region (north of Xuzhou) on wheat seedlings (AK-58), a box experiment was conducted and compared to an area far from the OAFCR (south of Xuzhou). The study revealed that (1) compared to the southern suburb of Xuzhou, the fresh and dry weight, activities of photosynthetic enzymes and POD of wheat seedlings in the OAFCR reduced obviously. (2) Significantly higher levels of Cr, Cd, Pb, Zn, and Cu were found in the shoots and roots of wheat seedlings in the OAFCR, with lower transfer factor for heavy metals (except Cd and As) in comparison to those in the southern suburb. And the bioconcentration factors of heavy metals (except As) in wheat seedlings in the OAFCR were significantly higher. (3) Nearly 90% of heavy metals (Pb, Cu, Cd, Zn, and Cr) absorbed by wheat were stored in cell walls and soluble fractions, with significantly higher contents of Cu and Cr in wheat seedlings' cell walls and higher contents of Pb, Zn, and Cd in soluble components found in the OAFCR. Our results showed that atmospheric deposition in the mining area has a certain toxic effect on wheat seedlings, and this study provides a theoretical basis for OAFCR crop toxicity management.

Application of Visible/Near-Infrared Spectroscopy and Hyperspectral Imaging with Machine Learning for High-Throughput Plant Heavy Metal Stress Phenotyping: A Review

Heavy metal pollution is becoming a prominent stress on plants. Plants contaminated with heavy metals undergo changes in external morphology and internal structure, and heavy metals can accumulate through the food chain, threatening human health. Detecting heavy metal stress on plants quickly, accurately, and nondestructively helps to achieve precise management of plant growth status and accelerate the breeding of heavy metal-resistant plant varieties. Traditional chemical reagent-based detection methods are laborious, destructive, time-consuming, and costly. The internal and external structures of plants can be altered by heavy metal contamination, which can lead to changes in plants' absorption and reflection of light. Visible/near-infrared (V/NIR) spectroscopy can obtain plant spectral information, and hyperspectral imaging (HSI) can obtain spectral and spatial information in simple, speedy, and nondestructive ways. These 2 technologies have been the most widely used high-throughput phenotyping technologies of plants. This review summarizes the application of V/NIR spectroscopy and HSI in plant heavy metal stress phenotype analysis as well as introduces the method of combining spectroscopy with machine learning approaches for high-throughput phenotyping of plant heavy metal stress, including unstressed and stressed identification, stress types identification, stress degrees identification, and heavy metal content estimation. The vegetation indexes, full-range spectra, and feature bands identified by different plant heavy metal stress phenotyping methods are reviewed. The advantages, limitations, challenges, and prospects of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping are discussed. Further studies are needed to promote the research and application of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping.

Biostimulant and Bioinsecticidal Effect of Coating Cotton Seeds with Endophytic Beauveria bassiana in Semi-Field Conditions

Increasing commercial demands from the textile and food industries are putting strong pressure on the cultivation of cotton and its derivatives to produce high yields. At the same time, cotton has high nutrient and irrigation requirements and is highly susceptible to insect pests. Coating cotton seeds with beneficial fungi could address these problems. The aim of this study was to investigate the growth of cotton using (A) conventional seeds and (B) seeds coated with the entomopathogenic fungus Beauveria bassiana (Hypocreales: Cordycipitaceae). The experiment was conducted in a greenhouse of the Department of Agriculture of the University of Ioannina, in a completely randomized design. The growth characteristics of cotton plants were recorded weekly while the fresh weight and dry matter of the leaves, shoots and roots of the developed cotton plants were calculated at the end of the experiment. Weekly determination of total chlorophyll content (TCHL) was used as an indicator of plant robustness during the 80-day experiment. Many cotton growth parameters of treated plants, like number of leaves, shoots and apical buds, plant height, stem diameter, fresh and dried biomass and TCHL, were significantly higher than those of the untreated ones. Apart from plant growth, naturally occurring by Aphis gossypii (Hemiptera: Aphididae) infestation which also monitored for six weeks. A significantly lower aphid population was recorded for inoculated plants after the fifth week compared to the control. The overall evaluation revealed that B. bassiana coating treatments appear to have a significant biostimulatory and bioinsecticidal effect. Our results could represent responsive applications to the demands of intensive cotton growing conditions.